Update app.py

app.py

@@ -11,28 +11,10 @@ from datetime import timedelta, datetime
 from branca.element import Figure
 
 from functions import decode_features, get_weather_data, get_weather_df, get_weather_json_quick
-#import functions
+##################
 
 def greet(total_pred_days):
-
-    
-#   print("hi")
     project = hopsworks.login()
-#   print("connected")
-#   #api = project.get_dataset_api()
-#   
-
-#   # The latest available data timestamp
-#   start_time = 1649196000000
-
-#  # end_time = 1670972400000
-
-    #start_date = datetime.now() - timedelta(days=1)
-    #start_time = int(start_date.timestamp()) * 1000
-
-    #print("Time Stamp Set. ")
-
-    #print("latest_date")
     
     mr=project.get_model_registry()
     model = mr.get_model("temp_model_new", version=1)
@@ -40,37 +22,37 @@ def greet(total_pred_days):
     
     model1 = mr.get_model("tempmax_model_new", version=1) 
     model_dir1=model1.download()
-
     model2 = mr.get_model("tempmin_model_new", version=1)
     model_dir2=model2.download()
     
     model = joblib.load(model_dir + "/model_temp_new.pkl")
     model1 = joblib.load(model_dir1 + "/model_tempmax_new.pkl")
-    model2 = joblib.load(model_dir2+ "/model_tempmin_new.pkl")
-
+    model2 = joblib.load(model_dir2+ "/model_tempmin_new.pkl"
     print("temp_model is now right")
-         
-    
-    #X = feature_view.get_batch_data(start_time=start_time)
-    
-    #latest_date_unix = str(X.datetime.values[0])[:10]
-    #latest_date = time.ctime(int(latest_date_unix))
-    # cities = [city_tuple[0] for city_tuple in cities_coords.keys()]
 
+    fs = project.get_feature_store()
+    weather_fg = fs.get_or_create_feature_group(
+        name = 'weather_fg',
+        version = 1
+    )
+
+    query = weather_fg.select_all()
+    Q = query.read()
     
     str1 = ""
     
     if(total_pred_days == ""):
         return "Empty input"
     
-    count = int(total_pred_days)
+    count = int(total_pred_days) 
     if count > 14:
         str1 += "Warning: 14 days at most. " + '\n'
         count = 14
     if count <0:
         str1 = "Invalid input."
         return str1
-
+    count = count + 1
+    
     # Get weather data
     fs = project.get_feature_store()
     print("get the store")
@@ -88,11 +70,8 @@ def greet(total_pred_days):
         # Get, rename column and rescale
         next_day_date = datetime.today() + timedelta(days=i)
         next_day = next_day_date.strftime ('%Y-%m-%d')
-        print(next_day)
         json = get_weather_json_quick(next_day)
         temp = get_weather_data(json)
-        print("Raw data")
-        print(temp)
         X = X.append(temp, ignore_index=True)
     
     
@@ -106,43 +85,32 @@ def greet(total_pred_days):
     X.drop('moonphase', inplace = True, axis = 1)
     X.drop('description', inplace = True, axis = 1)
     X.drop('icon', inplace = True, axis = 1)
-    
-    X = X.drop(columns=["datetime", "temp", "tempmax", "tempmin", "sunriseEpoch", "sunsetEpoch", "source", "datetimeEpoch", ]).fillna(0) 
+    X = X.drop(columns=["sunriseEpoch", "sunsetEpoch", "source", "datetimeEpoch"]).fillna(0) 
     X = X.rename(columns={'pressure':'sealevelpressure'})
-    X = X.drop(columns = ['conditions'])
-    print("Check dataframe")
-    print(X)
-    print("Data batched.")
-    # Rescale
-    #X = decode_features(X, feature_view=feature_view)
-    # Data scaling
-    #category_cols = ['name','datetime','conditions', 'tempmin', 'tempmax', 'temp']
-
-    #mapping_transformers = {col_name:fs.get_transformation_function(name='standard_scaler') for col_name in col_names if col_name not in category_cols}
-    #category_cols = {col_name:fs.get_transformation_function(name='label_encoder') for col_name in category_cols if col_name not in ['datetime', 'tempmin', 'tempmax', 'temp']}
-
-    #mapping_transformers.update(category_cols)
     
+    # Merge X and query
+    Y = X.append(Q, ignore_index=True)
     
     # Data scaling
-    #category_cols = ['conditions']
+    Y = Y.drop(columns = ['conditions', 'name', "datetime", "temp", "tempmax", "tempmin"])
+    category_cols = ['conditions']
     cat_std_cols = ['feelslikemax','feelslikemin','feelslike','dew','humidity','precip','precipprob','precipcover','snow','snowdepth','windgust','windspeed','winddir','sealevelpressure','cloudcover','visibility','solarradiation','solarenergy','uvindex']
     scaler_std = proc.StandardScaler()
-    #scaler_lb = proc.LabelEncoder()
-    X.insert(19,"conditions",0)
-    X[cat_std_cols] = scaler_std.fit_transform(X[cat_std_cols])
-    #X[category_cols] = scaler_std.transform(X[category_cols])
-    X.insert(0,"name",0)
+    Y.insert(19,"conditions",0)
+    Y.insert(0,"name",0)
+    print(Y)
+    Y[cat_std_cols] = scaler_std.fit_transform(Y[cat_std_cols])
+    Y[category_cols] = scaler_std.fit_transform(Y[category_cols])
+    
     # Predict
-    preds = model.predict(X)
-    preds1= model1.predict(X)
-    preds2= model2.predict(X)
+    preds = model.predict(Y[0:count])
+    preds1= model1.predict(Y[0:count])
+    preds2= model2.predict(Y[0:count])
     
     for x in range(count):
         if (x != 0):
-            str1 += (datetime.now() + timedelta(days=x)).strftime('%Y-%m-%d') + " predicted temperature:      " +str(int(preds[len(preds) - count + x]))+ " predicted max temperature:      " +str(int(preds1[len(preds1) - count + x]))+ " predicted min temperature:      " +str(int(preds2[len(preds2) - count + x]))+"\n"
+            str1 += (datetime.now() + timedelta(days=x)).strftime('%Y-%m-%d') + " predicted temperature: " +str(float(preds[len(preds) - count + x]))+ "\npredicted max temperature: " +str(float(preds1[len(preds1) - count + x]))+ "\npredicted min temperature: " +str(float(preds2[len(preds2) - count + x]))+"\n"
     
-    #print(str1)
     return str1
 
 demo = gr.Interface(fn=greet, inputs = "text", outputs="text")